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EnglishThe importance of mutual monitoring in recommender systems based on learning agents derives from the consideration that a learning agent needs to interact with other agents in its environment in order to Improve its individual performances. In this paper we present a novel framework, called EVA, that introduces a strategy to improve the performances of recommender agents based on a dynamic computation of the agent's reputation. Some preliminary experiments on real users show that our approach, implemented on the top of some well-known recommender systems, introduces significant improvements in terms of effectiveness.
| Titolo: | Dynamically Computing Reputation of Recommender Agents with Learning Capabilities | |
| Autori: | ||
| Data di pubblicazione: | 2008 | |
| Serie: | ||
| Handle: | http://hdl.handle.net/20.500.12318/14007 | |
| ISBN: | 978-354085256-8 | |
| Appare nelle tipologie: | 4.1 Contributo in Atti di convegno |
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